Everything you know about selecting fonts for data visualization is wrong, part 1
Ok, the title’s a little click-baity — but it’s probably accurate.
When my ADHD meets up with Adobe Fonts, many hours may pass as I seek font-pairing perfection and dive deeply into the stories of fascinating foundries. This isn’t “wasted” time — it’s fun!— but I do have deadlines to meet.
Recently, I tried to define a more systematic process for choosing fonts for data visualizations. My “process” heretofore has been to emulate other designers’ effective use of type in dataviz and in general, informed by my skills and expertise in other areas. (For example, I have a solid understanding of visual hierarchy and layout from having learned HTML and CSS.) Throw in some raw intuiton — et voilà, a process!
Before I did any additional learning or research, I listed what I thought I knew, kind of like when I mapped out everything I considered to be “good” UX for dataviz almost exactly a year ago. (Time is so strange, y’all.)
My baseline understanding of UX was better than my baseline understanding of typography, despite my longtime fascination with the latter. I basically regurgitated advice I’ve heard from a variety of sources:
- Use at least x-point typefaces. (Where x…
